Developing theories, models, and methods for the control of neural systems is not a new goal in neuroscience. Whether in support of basic science (e.g., seminal experiments from Hodgkin and Huxley) or in support of clinical therapies (e.g., technological development in brain-machine interfaces or deep brain stimulation), efforts to control neural activity have produced a plethora of experimental tools with varying levels of complexity [1]}. Building on these empirical advances, the development of a theory for control in neuroscience is a logical next step. Network control theory is one particularly promising option. In assimilating brain state and connectivity in a mathematical model [1]}, network control theory offers a first-principles approach to modeling neural dynamics, predicting its response to perturbations, and optimizing those perturbations to produce a desired outcome. In cellular neuroscience, network control theory has offered predictions of the functional role of individual neurons in C. elegans, and those predictions have been validated by perturbative experiments [3]}. While the theory has also offered predictions in humans [4]}, [5]}, [6]}, [7]}, [8]}, these predictions have not been validated in accompanying perturbative experiments. Here we address this gap by examining the utility of network control theory in predicting empirically recorded brain states, and by validating the fundamental assumption that state transitions are constrained by an individual's white matter connectivity. The work provides theoretical support for emerging empirical observations that structural connectivity can predict the behavioral effects of stimulation [9]}, thus constituting an important first step in establishing the promise and utility of control theoretic models of brain stimulation.
